Living Standards Working Paper No. 33 - World Bank...I. Vijverberg, Wim P. M. II. Title. III....
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Living Standards Measurement Study Working Paper No. 33 Wage Determinants in C6te d'Ivoire Jacques van der Gaag Wim Vijverberg FLE Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
Transcript of Living Standards Working Paper No. 33 - World Bank...I. Vijverberg, Wim P. M. II. Title. III....
Living StandardsMeasurement StudyWorking Paper No. 33
Wage Determinants in C6te d'Ivoire
Jacques van der GaagWim Vijverberg
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LSMS Working PapersNo. 1 Living Standards Surveys in Developing Countries
No. 2 Poverty and Living Standards in Asia: An Overview of the Main Results and Lessons of SelectedHousehold Surveys
No. 3 Measuring Levels of Living in Latin America: An Overview of Main Problems
No. 4 Towards More Effective Measurement of Levels of Living, and Review of Work of the United NationsStatistical Office (UNSO) Related to Statistics of Levels of Living
No. 5 Conducting Surveys in Developing Countries: Practical Problems and Experience in Brazil, Malaysia, andthe Philippines
No. 6 Household Survey Experience in Africa
No. 7 Measurement of Welfare: Theory and Practical Guidelines
No. 8 Employment Data for the Measurement of Living Standards
No. 9 Income and Expenditure Surveys in Developing Countries: Sample Design and Execution
No. 10 Reflections on the LSMS Group Meeting
No.II Three Essays on a Sri Lanka Household Survey
No. 12 The ECIEL Study of Household Income and Consumption in Urban Latin America: An Analytical History
No. 13 Nutrition and Health Status Indicators: Suggestions for Surveys of the Standard of Living in DevelopingCountries
No. 14 Child Schooling and the Measurement of Living Standards
No. 15 Measuring Health as a Component of Living Standards
No. 16 Procedures for Collecting and Analyzing Mortality Data in LSMS
No. 17 The Labor Market and Social Accounting: A Framework of Data Presentation
No. 18 Time Use Data and the Living Standards Measurement Study
No. 19 The Conceptual Basis of Measures of Household Welfare and Their Implied Survey Data Requirements
No. 20 Statistical Experimentation for Household Surveys: Two Case Studies of Hong Kong
No. 21 The Collection of Price Data for the Measurement of Living Standards
No. 22 Household Expenditure Surveys: Some Methodological Issues
No. 23 Collecting Panel Data in Developing Countries: Does it Make Sense?
No. 24 Measuring and Analyzing Levels of Living in Developing Countries: An Annotated Questionnaire
No. 25 The Demand for Urban Housing in the Ivory Coast
No. 26 The C6te d'Ivoire Living Standards Survey: Design and Implementation
No. 27 The Role of Employment and Earnings in Analyzing Levels of Living: A General Methodology withApplications to Malaysia and Tkailand
(List continues on the inside back cover)
Wage Determinants in C6te d'Ivoire
The Living Standards Measurement Study
The Living Standards Measurement Study (LSMS) was established by the World Bank in1980 to explore ways of improving the type and quality of household data collected bystatistical offices in developing countries. Its goal is to foster increased use of household dataas a basis for policy decisionmaking. Specifically, the LSMS is working to develop new methodsto monitor progress in raising levels of living, to identify the consequences for households ofpast and proposed government policies, and to improve communications between surveystatisticians, analysts, and policy makers.
The LSMS Working Paper series was started to disseminate intermediate products from theLSMS. Publications in the series include critical surveys covering different aspects of the LSMSdata collection program and reports on improved methodologies for using Living StandardsSurvey (LSS) data. More recent publications recommend specific survey, questionnaire and dataprocessing designs, and demonstrate the breadth of policy analysis that can be carried out usingLSS data.
LSMS Working PaperNumber 33
Wage Determinants in C6te d'Ivoire
Jacques van der GaagWim Vijverberg
The World BankWashington, D.C., U.S.A.
Copyright © 1988The International Bank for Reconstructionand Development/THE WORLD BANK
1818 H Street, N.WWashington, D.C. 20433, U.S.A.
All rights reservedManufactured in the United States of AmericaFirst printing May 1988
This is a working paper published informally by the World Bank. To present the results ofresearch with the least possible delay, the typescript has not been prepared in accordance withthe procedures appropriate to formal printed texts, and the World Bank accepts no responsibilityfor errors.
The findings, interpretations, and conclusions expressed in this paper are entirely those of theauthor(s) and should not be attributed in any manner to the World Bank, to its affiliatedorganizations, or to members of its Board of Executive Directors or the countries they represent.Any maps that accompany the text have been prepared solely for the convenience of readers; thedesignations and presentation of material in them do not imply the expression of any opinionwhatsoever on the part of the World Bank, its affiliates, or its Board or member countriesconcerning the legal status of any country, territory, city, or area or of the authorities thereof orconceming the delimitation of its boundaries or its national affiliation.
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The most recent World Bank publications are described in the catalog New Publications, a newedition of which is issued in the spring and fall of each year. The complete backdist of publicationsis shown in the annual Index of Publications, which contains an alphabetical title list and indexes ofsubjects, authors, and countries and regions; it is of value principally to libraries and institutionalpurchasers. The latest edition of each of these is available free of charge from the PublicationsSales Unit, Department F, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A.,or from Publications, The World Bank, 66, avenue d'I6na, 75116 Paris, France.
Jacques van der Gaag was acting chief of the Living Standards Unit of the DevelopmentResearch Department, the World Bank, when this paper was written. Wim Vijverberg was assistantprofessor at the University of Texas at Dallas and worked as a consultant for the Living StandardsUnit.
Library of Congress Cataloging-in-Publication Data
Gaag, J. van der.Wage determinants in Cote D'Ivoire / Jacques van der Gaag, Wim
Vijverberg.p. cm. -- (LSMS workino paper, ISSN 0253-4517 ; no. 33)
Includes bibliographies.ISBN 0-8213-1058-51. Wages--Ivory Coast--Effect of inflation on--Econometric models.
I. Vijverberg, Wim P. M. II. Title. III. Series.HD5096.9.G3 1988331.2'96668--dcl9 88-14095
v
ABSTRACT
The following two papers present an analysis of wage determinants inC8te d'Ivoire, using the standard Mincerian framework. The data used stemfrom the C6te d'Ivoire Living Standards Survey, conducted in 1985. Thissurvey collected information on 1,600 households. Our sample consists of the514 individuals in these households who reported a wage earning job during theseven days prior to the interview.
The first paper uses the total sample and addresses the issues ofcredentialism and returns to years of schooling, by type of school. In theregressions that do not include variables to represent school diplomas, wefind an unusual result: rates of return to one year of additional schoolingincrease with the level of schooling: almost 12 percent for elementaryeducation, but 20 percent for high school and 22 per cent for universityeducation. This pattern suggests a severe shortage of Ivorians with highereducation. The results by age-cohort (presented in Appendix 2) seem tounderscore this point: younger workers receive higher returns than their oldercounterparts. Apparently, the development of the Ivorian economy, and thecorresponding increase in the demand for better educated workers, has outpacedthe supply of such workers.
When diplomas acquired are added to the equation, the high returns toan additional year of schooling decrease substantially while the diplomas showa large impact on the wage rates (40-50 percent). This suggest the existenceof a certain amount of credentialism in the Ivorian wage sector. However, apure credentialistic specification of the wage equation is rejected by thedata.
Appendix 2 to the first paper reports results by cohort, sex,nationality and region.
The second paper reports the results for public and private workersseparately. However, rather than relying on standard OLS results for eachgroup, we develop a model that recognizes the endogeneity of the sectorchoice. We find that the OLS results are likely to be seriously biased. Theoverall dominance of public over private wages (indicated by the OLS results)vanishes once the selection process is taken into account. Public wages arestill somewhat higher for better educated workers, but the private sectoroffers higher wages than the government to workers with little education.
We finally show the importance of school diplomas as determinants forobtaining a job in the public sector.
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ACKNOWIEDGMENTS
These two papers benefited greatly from numerous comments made by
colleagues working in various parts of the World Bank: for the first, special
thanks go to George Psacharopoulos for his insightful and detailed comments.
We like to thank Morty Stelcner for his useful comments and suggestions on the
second paper. We also thank Kalpana Mehra for her excellent programming
assistance, Carmen Martinez for cheerfully typing the various drafts and
Brenda Rosa for her competence in putting together this final version.
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TABLE OF CONTENTS
I. WAGE DETERMINANTS IN COTE D'IVOIRE:EXPERIENCE, CREDENTIALS AND HUMAN CAPITAL
Page
1. Introduction ..........................................
2. The Basic Model; Returns to Schooling and Experience.....o........ 3Table 2.1: Regression Results Basic Log Wage Equation ....... 5
3. Returns to Schooling by Type of Schooling and Experience ........ O...7Table 3.1: Years of Schooling and Experience by Type.........7Table 3.2: Regression Results Extended Log Wage Equation ....8
4. Do Diplomas Mte?....................................lTable 4.1 Diplomas Acquired..........................12Table 4.2: Regression Results Extended Model, IncludingVariables on Diplomas Received .............................13
5. Summary and Conclusion....*...*.*..** .........................15
NOTES .................................. ......... 17Appendix 1 .............................................. 19ASppendix 2 .............. ...... *.0.*.0.......................19Rveferences .......................... ......... O..* ...... 0....... ..... O..* .... 19
II. A SWITCHING REGRESSION MODEL FOR WAGE DETERMINANTSIN THE PUBLIC AND PRIVATE SECTORS OF A DEVELOPING COUNTRY
1, Introduction ....... ..... ...... ..*...*.........................28
2. Who Gets the Public Job? .... .....................................30Table 2.1: Definitions and Summary Statistics of theVariables by Public and Private Sector Employment* ........934
3. Estimation Rsls.................................. .0.0-....35
3.1 The Wage Equation................................. ......... 36Table 3.1: FIML and OLS Estimates of Log Wage Equations
for the Public and Private Sectors ...... es ............. 37Table 3.2: FIML Estimates of Log Wage Equations for thePublic and Private Sector, with Restrictions on Diplomaand Basic Skill Variables.. ............ .. .. ... 39
3.2 The Switching Equation........... *. ............. ........................ . 40Table 3.3: Estimates of the Switching Equation..............41
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4. Summary and Discussion.............................................42Table 4.1: Variations in the Probability of Obtaining aPublic Job and in the Differences in the Log Wage Offers,Mean Observed Log Wages and Overall Average Log Wagesamong Ivorian Employees.... . . . . . . .. . . .. . . .. .... ... .. 43
Table 4.2: Non-Wage Benefits for Private and Public
NOTES ............................................................ 47
References ..... .. .. o... .... .. o. -*o *. ... *.o *o -o-49
I. WAGE DETERMINANTS IN COTE D'IVOIRE:EXPERIENCE, CREDENTIALS AND HUMAN CAPITAL
1. Introduction
This paper investigates the determinants of wages in Cote d'Ivoire,
using the well-known Mincerian framework. As such it presents yet another
piece of evidence regarding the importance of education and experience as
determinants of an individual's productivity, which, using a conventional
assumption, is measured by the hourly wage rate. At the same time, it suffers
from a number of shortcomings usually present in the economic literature on
this topic.
First of all, the sample consists of wage earners only. Thus, all
results should be interpreted as conditional upon having a wage earning job
and extrapolations to other economic activities (selfemployment, agriculture)
should be avoided. Secondly, following the standard Mincerian framework, the
analysis takes the two key variables, education and experience, as given. A
more structural analyses would, for instance, treat education as endogenous
and investigate the factors that determine schooling enrollment. Still
despite these shortcomings, we expect the results presented to be of interest
for a variety of reasons. The general importance of the role of human capital
in issues of development and distribution is sufficiently recognized. (See
King, 1980 for an excellent summary of this issue. Also World Development
Report, 1980.) Perhaps more importantly, our study is one of the few that
uses data from a sub-Saharan country. Of course, within the sub-Saharan
region, Cote d'Ivoire is not a "typical" country. Its real growth in GDP
increased 7.9 percent per year from 1965 to 1975, during which period economic
growth was driven by rapid expansion of export crops. This exceptional growth
patterns was continued from 1975 to 1980 (6.4 percent GDP increase per year),
but now fueled by rapid expansion of public investment. And though this
-2-
unprecedented "miracle ivorien" was followed by a severe recession, its per
capita GNP of U.S.$720 (1983) is well above that of its neighbors. Still, to
have estimates of, for example, returns to schooling for that part of the
world, is likely to benefit the discussions regarding growth oriented policies
in general, and public policies on education in particular.
With respect to public policies on education, Cote d'Ivoire presents
a particularly interesting case: more than 40 percent of the recurrent
government budget is spent on education. Thus even though our analysis is
only partial, it will shed some light on the rationale of this exceptionally
strong public emphasis on investments in education.
The outline of the paper is as follows: in the next section we
discuss the data, give background information on schooling and literacy in
C6te d'Ivoire and present the estimation results of a basic Mincerian log wage
equation. This basic model is extended in Section 3 to allow for differential
effects by type of schooling and by type of experience. We also address the
question of whether elementary schooling per se, or the acquired cognitive
skills (as measured by literacy and numeracy) are the determining factors of
wage differentials. In Section 4 we look at the relative importance of years
of schooling versus diplomas obtained. Here we will enter the discussions on
screening and credentialism that are put forward as alternatives to the human
capital model. In Section 5 we discuss the results and conclude.!'
In Appendix 2 we present and briefly discuss re-estimates of the generalmodel of Section 4, by cohort, sex, nationality and region.
-3-
2. The Basic Model; Returns to Schooling and Experience
Data used in this study are drawn from the C6te d'Ivoire Living
Standards Survey (CILSS). This multipurpose survey, which aims at measuring
socioeconomic factors relevant to the living standards of Ivorian households,
started in February 1985. During the first 12 month period, 1600 households
are interviewed, 688 in urban and 912 in rural areas. These 1600 households
form a random sample of the Ivorian population. Non-African expatriates are
excluded from the sample. In the second year 50 percent of the households
will be reinterviewed and 800 new households will replace the other half of
the sample. The survey is scheduled to be conducted on a permanent basis.
The current analysis is based on first year data only. 1/
The vast majority of the economically active population in C6te
d'Ivoire is self-employed, both in agricultural and non-agricultural
activities. Only a small percentage of the population reports having a wage
earning job. Not surprisingly, wage earners are concentrated in the urban
areas: in the capital, Abidjan, 17.2% of the labor force are wage earners,
9.7% and 1.4% in other urban areas and in rural Cote d'Ivoire, respectively.
The CILSS collects data on primary, secondary and tertiary jobs,
using recall periods of one week and one year. In this paper we restrict the
sample to all individuals who report a wage earning job as their primary
activity during the past 7 days. 2/ Reported earnings (generally reported per
month) were divided by hours worked per day times days worked per month 3/ to
obtain an hourly wage rate. Wages include the cash value of in-kind income.
4/ In our sample the total hourly compensation averages CFA 836, with a
standard error of 1354. The natural log of the hourly wage will be the
dependent variable throughout this study.
- 4 -
The analysis focuses on two sets of exogenous variables: education
and experience. As stated in the introduction, education is of major public
concern in C6te d'Ivoire, absorbing 42 percent of the recurrent government
budget. Still the overall literacy rate is only 37.6 percent, but the data
indicate that progress has been made. For younger cohorts, e.g. between 15
and 24 years of age, literacy ranges from 78.3 percent in the capital, to 59.3
percent in the villages. Years of schooling averages just 2.6 over the entire
population. Younger cohorts have almost 7 years of education in Abidjan, but
just 1.5 years in the villages. Given the national averages, our sample of
wage earners shows a fairly high education level: 6.87 years. In addition
the sample averages 1.03 years of technical training. The literacy rate is 74
percent. Clearly education encourages the decision to seek employment in the
wage sector, underscoring our previous warning to interpret the results as
conditional upon being a wage earner.
In the subsequent analyses we will use various measures of
experience. The most general one has been defined as age minus years of
schooling (including technical training) minus 5. Thus calculated, experience
averages 21.33 years, in a sample with an average age of 33.20 years.
The basic equation used to explain differences in the observed hourly
wages reads:
2ln Y = ao + a S + a2 E + a3 E
with Y, hourly wage rate
S, years of schooling
E, experience.
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Table 2.1 shows estimation results of this equation. The schooling
variable is split into years of academic (i.e. general curriculum) schooling
and years of technical training. Nationality and sex are added as
regressors. Sixteen percent of the sample is non-Ivorian, nineteen percent
of the wage earners are female.
Table 2.1: Regression Results Basic Log Wage Equation;(T-Values in Parenthesis)
Intercept 3.363 (18.84)
NAT , nationality; 0 = Ivorian, 1 = Other -.120 (1.59)
SEX , 0 = male, 1 = female -.002 (.02)
YRSCH , years of schooling .207 (22.76)
YRSTECH , years of technical training .113 (4.63)
GEXPER1 , general experience .053 (4.38)
GEXPER1Q , general experience squared, *1000 -.082 (.36)
R2, adjusted .585
The estimation results show a familiar picture: schooling and
experience are important determinants of wage differentials. Returns to an
additional year of schooling are very high, 20 percent. Psacharopoulos (1985)
reports an average of 13 percent for studies that used data from African
countries. These countries (Ethiopia, Kenya, Morocco and Tanzania) are, with
one exception (Morocco), considerably poorer than Cote d'Ivoire. Furthermore,
Psacharopoulos shows that returns to education have a tendency to decline with
economic development (as measured by growth in per capita income). Thus the
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Ivorian results seem to be out of line with "average" rate-of-return
estimates.
Technical training shows an 11 percent return for each year of
training, well below the result for academic schooling, as expected, but still
of considerable magnitude. The experience variables show an essentially flat
5 percent increase in the hourly wage rate for each year of experience. The
coefficients for Nationality and Sex are not significantly different from
zero.
In the next Section we will show how these results hold up when years
of schooling is differentiated by level of schooling and when general
experience is broken down into two components: occupation-specific and other
experience.
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3. Returns to Schooling by Type of Schooling and Experience
The traditional schooling system in C6te d'Ivoire includes 6 years of
elementary school, 4 years of junior high school, 3 years of senior high
school and a university program. Table 3.1 presents the average years of
schooling in our sample for each level of education. Of the 6.87 years of
total schooling, only a very small part (.39 years) is university training,
while most of its (4.20 years) is elementary education.
Table 3.1: Years of Schooling and Experience by Type
StandardMean Deviation
YRSED-EL , years of schooling, elementary school 4.20 2.67
YRSED-H1 , years of schooling, junior high school 1.73 1.84
YRSED-H2 , years of schooling, senior high school .54 1.10
YRSED-UN , years of schooling, university .39 1.38
YRSTECH , years of technical training 1.03 1.63
YRSAPP , years of apprenticeship .78 1.87
GEXPERM2 , general experience unrelated to currentoccupation 11.76 9.34
EXPERM2 , experience in current occupation 8.93 8.05
The data allowed us to differentiate total experience into experience
related to the current occupation and other general experience. Occupation-
specific experience is broader than tenure on the current job, as it includes
work experience in previously held jobs that have the same job description as
the current one. The results indicate a fair amount of job mobility, with
more than 11 years of experience (out of a total of 21) not related to the
current occupation. We now reestimate the log wage equation to detect the
differential impact of type of schooling and type of experience.5/
As before, the experience measures are also included in quadratic form
(EXPERM2Q and GEXPERM2Q). Results are given in Table 3.2, column 1.
Table 3.2: Regression Results Extended Log WageEquation (T-values in Parenthesis)
_______ _______ ______( 1 ) (2)
Intercept 3.840 (21.12) 3.813 (20.90)
NAT -.137 (1.34) -.117 (1.15)
SEX .021 (.23) .021 (.23)
YRSED-EL .119 (5.90) .074 (2.10)
YRSED-Hl .209 (6.35) .208 (6.31)
YRSED-H2 .208 (4.25) .208 (4.25)
YRSED-UN .227 (6.79) .227 (6.79)
RRR - _ .104 (1.53)
YRSTECH .085 (3.35) .085 (3.34)
YRSAPP .001 (.08) -.005 (.25)
EXPERM2 .112 (8.65) .111 (8.58)
EXPERM2Q * 1000 -1.973 (4.29) -1.957 (4.26)
GEXPERM2 .021 (1.74) .020 (1.67)
GEXPERM2Q * 1000 .105 (.35) .122 (.41)
R2, Adjusted .620 .621
- 9 -
Surprisingly, rates of return to one year of schooling increase with
the level of schooling: almost 12 percent for elementary education, but 20
percent for high school and 22 percent for university education. These
results, which are estimated quite precisely, run counter to much of the
evidence that the largest productivity differential is between primary school
graduates and illiterates, rather than between high school and primary school
graduates. Of course, literacy and numeracy can be acquired without attending
elementary school, while some persons with elementary education may remain
illiterate. We reestimate the equation, controlling for literacy and numeracy
as measured by ability to read, write and do simple arithmetic (the three
R's). The variable, RRR, is zero if the individual cannot write or read or do
arithmetic, and increases with I for each of the skills acquired. 61 The
estimation results (Table 3.2, column 2) show a positive effect of these
cognitive skills on the wage rate, though the effect is not measured very
precisely. 71 Note that the point estimate indicates a 31 percent
differential between illiterates (RRR = 0) and those with all three skills
(RRR = 3). 3/ At the same time returns to elementary schooling reduce from 12
percent to 7 percent per year. Thus the overall effect remains the same:
completed elementary schooling (6 years) produces a 73 percent overall wage
differential. Compared to those without formal schooling, but with reading
and writing skills, however, the differential is only 42 percent. All other
estimates remain the same.
The coefficients for experience indicate that the overall flat 5
percent per year increase (estimated in section 2) is a combination of an 11
percent return on occupation-specific experience and 2 percent on general
experience. Moreover, there is some curvature in the occupation-specific
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experience profile; the peak is reached after almost 30 years, an EXPERM2
level reached by only 8 individuals is our sample. Thus, we find a relatively
steep experience curve that flattens out over time. It is worthwhile to take
a closer look at this experience profile. Many authors have shown that
experience profiles are steeper for the educated workers, and have proposed
various explanations (e.g. Knight and Sabot, 1983). To test for this, we
interacted total years of education with the two experience measures that
enter linearly and quadratically in the equations. The results indeed
indicate steeper experience profiles for those with more schooling, at least
during the early years. 91
We finally note the importance of distinguishing between
occupational-specific and general experience. The results indicate that
someone who looses his job and has to start a career in a new occupation,
suffers an income loss during the early years, experiences a fairly long
period necessary to catch up and an income gain at the end of the working life
span. Though this may suggest that job mobility pays at the end, present
value calculations (with a 10 percent discount rate) always indicated a
welfare loss for the person who changes professions. As mentioned above, the
experience data show a fair amount of occupational mobility. Our calculations
indicate that this is likely to be the result of involuntary job losses,
rather than a profitable strategy to maximize lifetime income.
- 11 -
4. Do Diplomas Matter?
In the previous section we measured schooling as years of
education. Obviously, for the human capital model, output measures of the
education process (skills acquired) are the preferred variables, but such
variables are only rarely available. Boissiere et al. (1985), who do have
test scores in addition to wage information, conclude that acquired skills
rather than years of schooling cause the observed wage differentials. Our
results of Section 3 show the relative importance of literacy and numeracy.
While the importance of cognitive skills supports the human capital
model, evidence of a separate impact of school diplomas received may lend
credit to the screening or credentialist explanations that are often put
forward as alternatives to the human capital model. (See for instance Layard
and Psacharopoulos, 1974, and Riley, 1979). Completed schooling can indicate
ability and motivation, and thus schooling is used by employers to screen for
these desirable attributes. Productivity differentials associated with
differences in schooling are then thought to be caused by these basic
attributes rather than by schooling investments in human capital. In the
absence of data from explicit skills tests that accurately measure human
capital, we are not able to conduct a conclusive test to distinguish between
the human capital and the screening hypothesis. However, by including in the
equation information on completed schooling (i.e. diplomas acquired) in
addition to years of schooling, we can show the relative importance of these
variables in explaining wage differentials. If years of schooling can be
viewed as a proxy for investment in human capital, while diplomas are the
"signals" to employers regarding ability and motivation, then the regression
- 12 -
results show the relative merits of the human capital and the screening
hypothesis.
For these reasons we include in the regressions a set of dummy
variables indicating whether the individual has one of the four following
diplomas: elementary school, junior high school, any higher diploma 10/
and/or a technical diploma. Note that the first three diplomas are
cumulative, e.g., if you have a junior high school diploma you also have an
elementary school diploma. Those with a technical diploma may or may not have
one of the other diplomas. Unfortunately the data do not contain enough
information to be absolutely sure about this. We have assigned the
traditional diplomas to those with a technical diploma, if they have at least
the appropriate years of traditional schooling to make that plausible. Table
4.1 gives summary statistics of these new variables.
Table 4.1: Diplomas Acquired
StandardMean Deviation
DIP-EL, Elementary School .311 .463
DIP-HI, Junior High School .089 .285
DIP-UPP, Senior High School and Above .042 .202
DIP-TECH, Technical Training .313 .464
Regression results are presented in Table 4.2 column 1. To ease
comparison we repeat (in column 2) the results of Section 3.
- 13 -
Table 4.2: Regression Results Extended Model,Including Variables on Diplomas Received (T-Values in Parenthesis)
(1) (2) (3)
Intercept 3.774 (21.07) 3.813 (20.90) 3.859 (22.84)
NAT -.117 (1.15) -.118 (1.15) -.098 (.93)
SEX .011 (.12) .021 (.23) .059 (.65)
YRSED-EL .023 (.59) .074 (2.10) - -
YRSED-Hl .088 (2.07) .208 (6.31) - -
YRSED-H2 -.032 (.31) .208 (4.25) - -
YRSED-UN .208 (5.96) .227 (6.79) - -
RRR .113 (1.70) .104 (1.53) .141 (3.05)
DIP-EL .494 (3.05) - - .753 (5.78)
DIP-Hl .594 (3.60) - - .840 (6.96)
DIP-UP .536 (1.80) - - .979 (7.55)
DI-TECH -.011 (.10) - - -.042 (.43)
YRSTECH .072 (2.56) .085 (3.39) - _
YRSAPP -.002 (.11) -.005 (.25) - -
EXPERM2 .107 (8.46) .111 (8.58) .111 (8.48)
EXPERM2Q * 1000 -1.909 (4.23) -1.957 (4.26) -2.034 (4.36)
GEXPERM2 .026 (2.15) .020 (1.67) .016 (1.35)
GEXPERM2Q * 1000 .020 (.06) .122 (.41) .187 (.62)
R2, Adjusted .635 .621 .607
- 14 -
Comparing column (1) and (2) we see that the effect of years in
elementary school is no longer significant, if an elementary school diploma is
added to the equation. All that remains is the effect of cognitive skills
(RRR, 11 percent) and the diploma (DIP-EL, 49 percent). 11! The effect of a
year in primary high school drops to 8 percent, but the coefficient for high-
school diploma is .594. Additional years of university education continues to
pay off (20 percent, only slightly lower than in column 2), while higher
diplomas are apparently very valuable. Technical diplomas do not make any
difference.
While these results lend credibility to credentialism (showing large
bonuses for obtained diplomas), returns to basic cognitive skills (the three
R's) and years in junior high school (YRSED-Hl), to years of technical
training (YRSTECH) as well as years of university training (YRSED-UN) are also
rewarded. These results, assuming that years of schooling indeed reflect
increases in human capital, support the validity of the human capital model.
The third regression (column 3), using only diplomas obtained as the
explanatory schooling variables, explains the wage rate significantly less
than the one that includes years of schooling as well (F = 6.92, significant
at 1 % level), thus rejecting a pure credentialist interpretation of the
results.
- 15 -
5. Summary and Conclusion
In this paper we presented the first estimates available for returns
to education in the Republic of C6te d'Ivoire. The analyses was restricted to
wage earners only. Not surprisingly, we found that the two variables usually
associated with wage differentials, experience and education, turned out to be
good predictors in the Ivorian case as well. What is surprising, however, is
the order of magnitude of the estimated returns to education. For instance,
the 20 percent per year returns for higher education is almost twice as high
as usually found. Furthermore, higher education showed a higher pay-off than
elementary schooling, a result that also runs counter to usual findings.
Since the wage sector is only a small part of the total labor market, it is
perhaps prudent not to draw strong policy conclusions from these
observations. Still, they do point in the direction of a shortage of schooled
personnel, especially with regard to higher education.
To the extent possible, we disaggregated the returns to schooling
into two effects: returns to skills and returns to credentials. The latter
turned out to be very high, though a pure credentialist interpretation of the
data was rejected. If indeed there is a shortage of schooled personnel, it
comes as a surprise to find a bonus for diplomas; employers who compete for
the scarce schooled employees should reward any augmentation in
productivity. Whether schooling is completed should not be the dominant
factor. Note, however, that 41 percent of the Ivorian wage sector is
government employment. If government wage policies are such that those with
completed schooling are rewarded, without regard to the marginal increase in
productivity due to the last year of schooling, our data will reflect this.
12/ Though the government's emphasis on education, especially higher
- 16 -
education, seems to adequately address the apparent shortage of schooled
workers, a policy of paying rents for educational credentials is economically
inefficient for the public wage sector, while at the same time hampering the
optimal distribution of scarce skilled labor resources in the labor market as
a whole. A more detailed study of this issue is likely to have a high pay-
off, especially in light of the government's strong public policy on
education.
Using our most extensive model we showed that it pays to hold on to
one's job, or at least not to change occupations. Given the large amount of
job mobility observed in the data, these results did highlight the
individual's welfare loss associated with losing a job.
We conclude by recalling a warning mentioned in the introduction:
all results are conditional upon being a wage earner. As such they refer to a
small part of the Ivorian labor market only. An analysis of the other sectors
of the labor market, including the question of how people sort themselves into
the various sectors (e.g. Summer, 1981; Vijverberg, 1986), will give a more
complete picture of the role of education and experience but is beyond the
scope of this paper.
- 17-
NOTES
To be precise, the current analysis draws from data obtained on 1588households interviewed between February 15, 1985 and February 15, 1986.Data on 12 households scheduled to be interviewed during this period ismissing.
2/ Secondary jobs are mostly agricultural activities or work in non-agricultural family enterprises. Calculating the returns to labor ofthese activities is notoriously difficult and well beyond the scope ofthis paper.
31 Or by the relevant period if earnings were reported per day, week, quarteror year.
41 Obviously, reporting errors and miscodings in hours and/or earnings canlead to some highly implausible hourly wages. Of the 522 individuals withwage earning jobs, 7 showed hourly wages of less than 10 CFA, and onereportedly worked as a teacher for 13082 CFA per hour for 4 hours aweek. These individuals were deleted after they were identified asoutliers using Cook's test. The total sample thus obtained includes 514individuals.
51 For completeness sake we also included years of apprenticeship in theequation, as an alternative to technical training.
6/ These skills are selfreported. The correlations between them are veryhigh: .86 between arithmetic and writing, and exceeding .90 for the othercases. We estimated some test regressions that include these skillsseparately, but the multicollinearity prevented us from separating theeffects.
7/ The T-value of 1.53 indicates a 12% - significance level.
8/ It is customary to refer to the coefficients in log wage equations as"percentage" differences, though this is not quite correct for discretevariables. See Halverson and Palmquist for the exact interpretation andcorrection factors.
9/ Full estimation results are presented in Appendix 1.
10/ We did not separate senior high diploma and the various universitydegrees, as these groups become very small.
/ Note once again that the point estimates for completed elementaryschooling add up to a 73 percent wage differential with illiterates. (11 +49 + 6 * 2.3)
21 A comparative study of wage determinants in the private and public sectorseparately can shed further high on this issue. However, since being inone sector rather than the other is endogenous to the wage earner, such astudy is beyond the scope of this paper (See van der Caag and Vijverberg,1986).
- 18 -
APPENDIX 1
Regression results with experience - education interaction terms.
Table Al: Regression Results; Log Wage Equationwith Education - Experience Interaction Terms
(T-Values in Parenthesis)
Intercept 3.235 (12.58)
NAT -.147 (1.46)
SEX .041 (.45)
YRSED-EL .205 (5.97)
YRSED-H1 .240 (6.53)
YRSED-H2 .235 (4.59)
YRSED-UN .233 (6.75)
YRSTECH .085 (3.36)
YRSAPP .012 (.60)
EXPERM2 .081 (4.17)
EXPERM2Q 1/ -.870 (1.27)
GEXPER2 .080 (4.00)
GEXPER2Q 1/ -.945 (2.31)
YRS 2/ * EXPERM2 1/ 4.072 (1.53)
YRS * EXPERM2Q _/ -.153 (1.51)
YRS * GEXPER2 1/ -6.888 (2.55)
YRS * GEXPER2Q _/ .088 (.77)
R 2, adjusted .639
1/ -'- 1000
2/ YRS =total years of formal schooling.
- 19 -
APPENDIX 2
Wage Equations by Age Group, Sex, Nationality and Region -/
In this Appendix we present estimates of the same wage equation
discussed in Section 4 (Table 4.2), for age cohorts (under and over 32 years),
males, females, Ivorians, non-Ivorians and by region (Abidjan, other urban and
rural).
In Table A.1 we present F-statistics for the test whether the results
differ significantly between the subgroups distinguished. For the 84 non-
Ivorians in the sample we find the wage equations to be the same as for
Ivorians. However, pairwise differences between the other groups are
statistically significant at the 5% level.
Table A.1: F-Statistics for Testing the Equality ofWage Equation Between Subsamples
F Significance level
Age groups (over and under 32) 1.602 5%
Sex 1.974 5%
Nationality 1.319 not significant
Region 2.868 5%
We will now briefly discuss the main differences detected.
/ We are indebted to Hailu Mekonnen who prepared all the statistical resultsof this Appendix.
- 20 -
Cohort Effects
Table A.2 shows estimates of the wage equations for young (less than
32 years) and older (32 and over) wage earners.
Out of the sample of 514 wage earners, 252 were below 32 years of
age. This group has an average of 7.60 years of schooling, 4.11 years of
current experience and 7.82 years of general experience. The corresponding
figures for the remaining 262 wage earners above 32 years of age are 6.18
years, 13.57 years and 15.55 years.
Table A.2: Wage Equations for Young and Old Workers
YOUNG OLD
INTERCEPT 3.351 (10.67) 4.371 (12.23)NAT .088 (.51) -.365 (3.14)SEX .213 (1.58) -.287 (2.36)YRSED-EL .023 (.34) .058 (1.21)YRSED-H1 .115 (1.72) .068 (1.24)YRSED-H2 .066 (.39) -.113 (.91)YRSED-UN .204 (3.77) .194 (4.21)BASICED .102 (.82) .096 (1.33)DIPCEPE1 .666 (2.80) .240 (1.13)DIPBEPC1 .660 (2.54) .434 (2.11)DIPUPPR1 .300 (.65) .810 (2.18)DIPTECH -.109 (.58) -.0006 (.005)YRSTECH .068 (1.43) .081 (2.41)YRSAPP .0007 (.02) .015 (.55)EXPERM2 .174 (3.46) .083 (4.72)EXPRM2Q -.006 (1.31) -.001 (2.68)GEXPERM2 .021 (.55) .006 (.35)GEXPM2Q .001 (.67) .0003 (.98)
R2, Adjusted .644 .668
- 21 -
Investment in formal schooling has been emphasized by the Ivorian
government as one of the strategies for sustained growth. Government
expenditures on education currently exceed 40 percent of the total budget,
more than found in any other country. Education of the younger cohort now
averages 7.58 years, while the literacy rate for this cohort is about 83
percent, compared to 73 percent for the older workers. Therefore, it is
interesting to see whether this increased supply of trained people has reduced
the returns to education, or whether the development of the Ivorian economy
has lead to an even greater shortage of educated personnel. As stated above,
the wage rate regressions for young (below 32 years of age; N = 252) and older
(32 and over; N = 262) wage earners are significantly different. However, the
differences appear in a somewhat unexpected way: despite the considerable
increase in education levels, rates of return to education for the younger
cohort are generally higher than for the older ones. This is especially true
for elementary and junior high school diplomas. Older workers, however,
benefit more from diplomas of higher education.
Another difference between the wage equations for the two cohorts is
that between male and female wage earners. While young women tend to be
somewhat favored as compared to their male counterparts older women receive 20
percent less (T = 2.35). In other words, while the overall picture did not
show any significant gender discrimination, the cohort specific equations show
a particularly interesting one.
- 22 -
Differentials by Gender
Of the 514 wage earners in our sample, 100 are women. Separate
estimates for male and female wage earners are presented in Table A.3. Wage
equations estimated for male and females separately are statistically
different at a 5 percent level. The differences among individual coefficients
show a mixed picture. Male non-Ivorians face discrimination on the basis of
their nationality (-.21; T = 1.96), while female foreigners are favored (.56;
T = 2.03). Returns to junior high school are larger for women. The
experience patterns are distinctly different: while males show the "overall"
pattern (i.e. 12 percent per year in the early years, with a peak after about
3- years), females receive a flat 9 percent a year return to job specific
experience.
Table A.3: Wage Equations for Male and Female Workers
Males Females
INTERCEPT 3.537 (17.94) 4.554 (11.36)NAT -.214 (1.96) .558 (2.03)YRSED-EL .041 (.99) -.113 (.81)YRSED-Hl .079 (1.67) .164 (1.72)YRSED-H2 .074 (.64) -.260 (1.20)YRSED-UN .211 (5.64) .136 (1.41)BASICED .109 (1.58) .109 (.46)DIPCEPE1 .528 (3.07) .683 (1.52)DIPBEPC1 .523 (2.75) .615 (1.88)DIPUPPR1 .223 (.68) 1.489 (2.08)DIPTECH .059 (.47) -.173 (.76)YRSTECH .073 (2.32) .102 (1.61)YRSAPP .004 (.19) .128 (1.03)EXPERM2 .117 (8.51) .093 (2.79)EXPM2Q -.002 (4.27) -.002 (1.77)GEXPERM2 .041 (2.63) -.016 (.63)GEXPM2Q -.0003 (.64) .0006 (1.31)
R2 Adjusted .673 .655
- 23 -
Wage equation by region
Separate wage equations for Abidjan, other urban areas and rural
areas are presented in Table A.4.
Regional Effects
The regional distribution of wage earners is that 280 live in
Abidjan, 179 live in other cities and 55 live in rural areas. Wage earners in
Abidjan have an average of 7.30 years of schooling, 8.99 years of current
experience and 11.47 years of general experience. The corresponding figures
for other cities is 6.98 years, 9.67 years and 11.52 years, respectively. In
rural areas, the wage earners had 4.34 years of schooling, 6.24 years of
current experience and 14 years of general experience. None of the wage
earners in the rural sample had university education.
As shown by the F-statistic in Table A.1 the wage equations for the
three regions are significantly different. It should also be noted that none
of the coefficients for the rural wage equation are significant. This may be
attributed to the relatively small rural sample. Comparisons will therefore
be made between the wage equation for Abidjan and that for other cities.
Both in Abidjan and other cities, university education has a rate of
return of 23 percent. The most striking result is the rate of return for
advanced diplomas in Abidjan which is 89 percent. This variable has an
insignificant coefficient for other cities. It is also noteworthy that the
rate of return to a CEPE diploma is 96 percent for other cities and only 38
percent for Abidjan. It appears that the market responds to apparent
shortages in well-trained employers in cities other than Abidjan.
- 24 -
Table A.4: Wage Equations for Workers in Abidjan, Other Cities andRural Areas
Abidjan Other Cities Rural Areas
INTERCEPT 4.009 (16.03) 3.247 (10.40) 3.893 (5.95)NAT -.174 (1.27) .285 (1.79) -.755 (1.60)
SEX -.062 (.53) .156 (1.13) -.006 (.01)YRSED-EL .055 (.98) -.034 (.55) -.131 (.62)
YRSED-H1 .021 (.34) .157 (2.55) .220 (.82)YRSED-H2 -.169 (1.19) .122 (.86) .489 (.83)YRSED-UN .234 (5.03) .233 (3.58) - -
BASICED .024 (.26) .205 (2.22) .385 (.90)DIPCEPE1 .377 (1.68) .962 (3.58) .393 (.53)DIPBEPC1 .700 (3.08) .414 (1.70) .199 (.22)DIPUPPR1 .890 (2.12) .087 (.22) -.846 (.50)DIPTECH -.100 (.64) .133 (.85) .563 (.88)YRSTECH .111 (3.13) .047 (.91) .004 (.03)YRSAPP -.024 (.85) .043 (1.24) -.042 (.33)
EXPERM2 .109 (6.14) .098 (5.26) .092 (1.31)EXPM2Q -.002 (3.30) -.002 (2.34) -.001 (.33)GEXPERM2 .035 (1.86) .034 (1.55) .031 (.66)GEXPM2Q -.0002 (.45) -.0001 (.14) -.00009 (.11)
R2, Adjusted .639 .730 .730 .633
- 25 -
Finally, we present in Table A.5 the wage equations for Ivorians and non-
Ivorians. Based on the F-test we could not reject the hypothesis that both
equations are statistically the same.
Table: Table A.5 Wage Equations for Ivorian and Non-Ivorian Workers
Ivorian Non-Ivorian
INTERCEPT 3.811 (19.03) 3.150 (6.10)SEX -.100 (1.05) .940 (2.97)YRSED-EL .028 (.63) .023 (.23)YRSED-H1 .080 (1.83) .175 (.70)YRSED-H2 -.020 (.19) .148 (.70)YRSED-UN .211 (5.96) .088 (.43)BASICED .109 (1.41) .118 (.81)DIPCEPE1 .479 (2.68) .247 (.52)DIPBEPC1 .575 (3.38) .576 (.62)DIPUPPRI .526 (1.77) -DIPTECH -.022 (.19) .286 (.53)YRSTECH .074 (2.48) .123 (1.10)YRSAPP -.023 (.87) .070 (1.67)EXPERM2 .117 (8.23) .073 (2.33)EXPM2Q -.002 (4.27) -.001 (.85)GEXPERM2 .022 (1.72) .078 (1.84)GEXPM2Q .0002 (.59) -. 001 (1.25)
R2, Adjusted .647 .597
- 26 -
REFERENCES
Boissiere, M., J. B. Knight, R. H. Sabot. Earnings, Schooling, Ability andCognitive Skills. American Economic Review Vol. 75, 5, 1985, pp. 1016-1030.
King, T. (Ed.). Education and Income. World Bank Staff Working Paper, No.402, July 1980.
Knight, J. B. and R. H. Sabot. Why Returns to Experience Increase witheducation. World Bank, Mimeo, 1983.
Layard, R. and C. Psacharopoulos. The Screening Hypothesis and Returns-to-Education, Journal of Political Economics, 82, September/October 1974, pp985-998.
Riley, John C. Testing the Educational Screening Hypothesis, Journal ofPolitical Economy, 87, October 1979, pp. S227-S252.
Psacharopoulos, G. Returns to Education: A Further International Update andImplications. Journal of Human Resources Sincerely yours, , Volume 20,Number 4, Fall 1985, pp. 583-597.
Sumner, Daniel A. Wage functions and Occupational Selection in a Rural LessDeveloped Country Setting, Review of Economics and Statistics, November1981, pp. 513-519.
Van der Gaag, Jacques and Wim P.M. Vijverberg (1986). A Switching RegressionModel for Wage Determinants in the Public and Private Sectors of aDeveloping Country, Mimeo.
Vijverberg, Wim P.M. Consistant Estimates of the Wage Equation whenIndividuals Choose Among Income-Earning Activities, Southern EconomicJournal 52, 4, April 1986, pp. 1028-1042.
World Development Report, 1980. The World Bank, August 1980.
II. A SWITCHING REGRESSION MODEL FOR WAGE DETERMINANTSIN THE PUBLIC AND PRIVATE SECTORS OF A DEVELOPING COUNTRY
- 28 -
1. Introduction
Ever since education has been recognized as an investment in human
capital, economists have attempted to estimate the rate of return to this
investment. Occasionally such studies take a broad view, either by looking at
the contribution of education to the overall growth of the economy (e.g.
Krueger, 1968), or by taking a very comprehensive view of the private benefits
of education that accrue to the individual (e.g. Haveman and Wolfe, 1984).
More often, however, rates of return to education are estimated as the
marginal contribution to an individual's productivity of one year of
education. Psacharopoulos' recent update of international studies in this l
area contains studies from no less than 61 countries (Psacharopoulos, 1985).
This Mincerian approach, which centers around the estimation of al
wage or earnings function, is based on the assumption that wages are set equal
to the marginal productivity of the wage earners, though--of course--there are
many reasons why this may not be the case. Non-competitive market forces may
influence the wage structure in many ways. Unionized workers are likely to)
receive wages that differ from their non-unionized counterparts. Minimum wage
legislation drives a wedge between marginal productivity and the wage rate.
Governments may pursue employment, distributional or other political policies
using the wage scale for public employees as a policy instrument. In general,
the larger the institutional or regulatory influence on the labor market, the
more likely there is a difference between the observed wage rate and the
worker's marginal productivity.
This problem is especially important in developing countries, where
it is common that the wage sector is dominated by public employment.
Consequently, the public wage bill forms a major part of the government budget
- 29 -
and comes under intense scrutiny in times of recession and adjustments.
Furthermore, if distortions exist, the working of the labor market in general,
and development policies that depend on a flexible market in particular, may
be seriously hampered by spillover effects of public employment policies on
the private sector.
The first question to answer - and the one we address in this paper -
is whether wage differentials between the public and the private sector
exist. This question has spun a sizable literature in the industrialized
world (e.g. Smith, 1977, Ganderson, 1979), but few studies exist for
developing cou.itries. Moreover the results of such studies are mixed.
Lindauer and Sabot (1983) conclude that a substantial wage premium
exists for public employees (in Tanzania), but Corbo and Stelcner (1983) find
a small bonus in the private sector (in Chile). Psacharopoulos (1985) reports
the difference in returns to schooling between the "competitive private"
sector and the "non-competitive public" one to be 3 percentage points on
average (13 and 10 percent, respectively). Unfortunately, a common
shortcoming of these studies is that they attempt to estimate wage
differentials by using one or more dummy variables to indicate the sector
where the individual is employed, or by estimating separate wage equations for
each sector. If the labor market is segmented in sectors that give different
awards for human capital, one of these sectors will be preferred by most
workers and entry in that sector is likely to be determined by factors other
than productivity. Ignoring the endogeneity of being in one sector or the
other may bias the estimates that are based on sector-specific samples. -
In this paper we will contribute to the Mincerian returns to
education literature and focus on public-private sector differentials. Data
- 30 -
stem from a recent survey conducted in the Republic of C6te d'Ivoire, where
41.1 percent of all wage earners work in the public sector.
The model developed in section 2 is similar to the one now commonly
used to study union/non-union wage differentials (e.g. Lee, 1978; Robinson and
Tomes, 1984). It consists of two wage equations and a "switching" equation
that determines in which sector the employee is working. Among other things,
the model shows the relative importance of human capital (years of education,
experience) versus credentials (diplomas acqui-red) in obtaining a public or
private job and in determining the wage. In section 2 we also describe the
data. In section 3 we present and discuss the estimation results. Sector 4
concludes.
2. Who Gets the Public Job?
If the labor market is segmented into a public and private sector,
there will be a shortage of jobs in the preferred sector, and non-price
rationing will determine entry into this sector. The selection process has
two steps: first an individual will determine whether or not to try to obtain
a public job. Secondly he may or may not be chosen for the job. The
likelihood of not being chosen forms a cost to the prospective employee, that
he compares with the expected benefits. The probability of obtaining a public
job depends on characteristics of the individual that are used by the employer
to choose a worker from the queue. We assume, for the time being, that
expected benefits are equal to the difference in the (log) wage rates between
the two sectors. Thus, an individual will be in the public sector if
(1) (in w1 - Qn w2) > X1 a1 e1
- 31 -
where wI , the wage rate in the public sector
W2 ^ the wage rate in the private sector
Xi , a vector of characteristics that are associated with the
probability of obtaining a public job
el , a disturbance term.
Note that this one equation summarizes the two-step process: first,
the expected wage differential has to be large enough for the individual to
make it worthwhile to try to obtain a public job and secondly the employee
needs to be chosen from the list of prospective candidates.
We now assume that wages in each sector are determined as follows:
(2) En w1 = Z YZ + u
(3) n w2 =Zy2 +u 2
where Z is a vector of wage determining variables. Substituting (2) and (3)
into (1), and assuming that all wage determining variables also influence the
probability of obtaining a public job, we have:
(4) I = 1 if XB + e > 0 (i.e., the individual has a public sector job)
I = 0 otherwise
Where e = ul - u2 - E and X absorbs all exogenous variables in Xi and Z.
Assuming normality of e, u1 and u2, maximum likelihood estimates of
the parameter vectors of interest, y1 and y2, can be obtained. Note that OLS
regressions on the public and private sample separately implicitly assume
coV (ul, E) = coV (u 2 , E) = 0 and thus are generally biased.
- 32 -
The wage equations have the standard Mincerian form (Mincer, 1974),
regressing the log of the hourly wage rate on a set of education, experience
and other exogenous variables. Education is measured by years of schooling at
each level of the schooling system. We also include years of technical
training and apprenticeship in the equation. Total work experience is divided
into experience related to the current occupation and other work experience.
Both terms are also entered in the regression in quadratic form.
In the literature on returns to education there is much debate on the
interpretation of the education effect. Does education indeed increase
productivity (the human capital school) or does schooling, especially
completed schooling, serve as a signal to employers about the innate ability
and motivation of the worker (the screening hypothesis). In order to test
whether completed schooling or years of schooling is the relevant wage
determining variable, we add to the wage equations whether or not the
individual has one or more of the following diplomas: elementary school,
junior high school, a higher diploma from traditional (i.e. general
curriculum) education, or a technical diploma. The first three diplomas are
cumulative. Note that adding both years of schooling and diplomas acquired,
does not constitute a strong test of the relative merits of the human capital
and screening hypothesis. For instance, government workers may receive a
bonus for a diploma for reasons not directly related to a perceived difference
(by the employer) in ability or motivation. On the other hand, able students
may decide not to finish their traditional schooling in order to take
advantage of better opportunities.
Direct measurements of a worker's abilities and skills are usually
lacking in studies of this kind, which probably accounts for the lack of
- 33 -
consensus in the literature on the relative merits of the human capital and
the screening model. Riley (1979) report results that support the screening
hypothesis, but Boissiere et al. (1985) in what is perhaps the only study for
the developing world that has such measures, show strong support for the human
capital theory, i.e. skill (productivity) differences rather than differences
in schooling input, determine wage differentials. Our data allow us to
include one measure of basic cognitive skills: reading, writing or simple
arithmetic. The measure takes the value zero for illiterates and increases by
1 for every skill. The human capital model would predict that this variable,
rather than years of elementary schooling will determine wage differentials.3/
We finally add sex and nationality to the equation to test for
discrimination on the basis of these attributes. 4/
Data used in this study stem from the C6te d'Ivoire Living Standards
Survey (CILSS). Details on this nationwide survey, which collected
information on 1600 households in 1985, can be found in Van der Gaag and
Vijverberg (1986) and Ainsworth and Munioz (1986). The survey contains
information on 513 individuals who report a wage earning activity as their
main job during the seven days prior to the interview. Our analysis is based
on this sample of wage earners.
Reported earnings (generally reported per month) were divided by
hours worked per day times days worked per month to obtain an hourly wage
rate. Wages include the cash value of in-kind income. Summary statistics of
the variables are presented in Table 2.1, for public and private workers
separately.
- 34 -
Table 2.1: Definitions and Summary Statistics of the Variables
by Public and Private Sector Employment
Private Sector Public Sector
N=301 N=212
Standard Standard
Symbol Definition Mean deviation Mean deviation
NAT Nationality: O=lvorian, 1=other .275 .44 .000 .00
SEX O=male, 1=female .149 .35 .259 .44
DIP-EL 0/1 if no/yes elementary school diploma .478 .50 .830 .38
DIP-HI 0/1 if no/yes junior high school diploma .182 .38 .491 .50
DIP-UPP 0/1 if no/yes higher diploma .089 .28 .236 .43
DIP-TECH 0/1 if no/yes technical diploma .202 .40 .472 .50
RRR Reading, writing and arithmetic skills 1.973 1,37 2.637 .94
YRS-EL Years elementary education 3.561 2.84 5.132 2.08
YRS-H1 Years junior high school 1.215 1,69 2.472 1.80
YRS-H2 Years senior high school .322 .89 .859 1.29
YRS-UN Years university .169 .83 .717 1.87
YRS-TECH Years technical training .734 1.58 1.462 1.61
YRS-APP Years apprenticeship 1.166 2.20 .241 1.08
EXPOCC Occupation specific experience 7.399 7.58 11.116 8.23
GEXPER General work experience 13.135 9.36 9.705 8.85
YRSCH Total years of schooling 5.269 4.94 9.179 5.26
AGE Age in years 32.554 10.16 36.566 8.70LNW Log of hourly wage rate 5.557 1.29 6.577 .99
These summary statistics foreshadow to some extent the estimation
results of the switching equation. Public employees are on average better
educated, showing 9.179 years of education versus 5.269 in the private
sector. Furthermore, the concentration of schooling diplomas is much higher
in the public sector. There are no non-Ivorians in the public sector, and
25.9 percent of the government labor force is female versus only 14.9 percent
in the private sector. Total experience, measured as age minus formal
schooling minus technical training minus 5, averages about 20 years in both
sectors. But occupation specific experience is much lower in the private than
- 35 -
in the public sector, showing the importance of job tenure in the latter and
of job mobility in the former. Note also that, with an average age of 32
years, 20 years of experience indicates very young entry in the private
sector.
Worthy of special notice is also the difference in the wage rates:
the difference in the means of lnW is 1.020 in favor of public sector
employees. Finally note that wages in the private sector show more variation
than in the public sector.
In the next section we will first present the estimation results of
the log wage equations for the two sectors. Then, using the expected wage
difference as an instrumental variable, we will estimate the structural
switching equation (i.e. equation (1)) as a probit equation.
3. Estimation Results
Full information maximum likelihood (FIML) 5/ estimates of the model
consisting of equations (2), (3) and (4) were obtained using the assumption
that (ul, u2, E) are N(0, z) where the covariance matrix E equals
a 11 a 12 ale= 012 022 a2e
All variables that enter equations (2) and (3) are also included in
equation (4), with two exceptions: first, to avoid multicollinearity, years
of schooling by level of school have been aggregated to total years of
schooling. 61 Secondly, since both experience variables are job specific, it
is somewhat tautological to use them in the switching regression. Therefore
we decided to replace the experience variables by the age (and age-squared) of
- 36 -
the worker. If there is queuing we expect a positive effect of age on the
probability of obtaining a public job, though the effect may diminish at
higher age levels.
3.1 The Wage Equations
Results of the FIML estimation for the two wage equations are
presented in Table 3.1, together with OLS estimates for the public and private
sector separately. We see, for both sectors, that diplomas are important in
determining wage differentials, with the exception of technical diplomas. The
effect is very large, for instance a 42 percent wage increase in the public
sector and a 61 percent increase in the private sector for a junior high
school diploma. The effect of the three basic cognitive skills (RRR) is
barely significant in the private sector, and not at all in the public sector.
7/ Note, however, that the effect of an elementary school diploma (highly
correlated with basic skills) is larger in the public sector.
The main question addressed in this study is whether the public and
private wage equations are different, in part or overall. We divided the set
of explanatory variables into three groups, related to (1) diplomas and RRR,
(2) years of schooling and technical training, (3) years of apprenticeship and
experience. We reestimated the model under the assumption that the
coefficients for any one or a combination of these groups of variables were
different for the public and private sector. Likelihood ratio test were used
to test whether the unrestricted model (Table 3.1) performed better than any
of the restricted ones. We could not reject the hypothesis that the diploma
variables and RRR, years of apprenticeship and the experience variables have
- 37 -
Table 3.1: FIML and OLS Estimates of Log Wage Equations
for the Public and Private Sectors
(t-values in parentheses)
FIML-Estimates* OLS-regressions
Public sector Private sector Public sector Private sector
INTERCEPT 2.841 (7.87) 3.482 (14.65) 4.819 (14.59) 3.320 (14.14)
NAT - - .285 (2.20) - _ -.124 (-1.05)
SSX -.125 (-1.04) .141 (.97) -.319 (-3.06) .312 (2.17)
DIP-EL .801 (2.50) .395 (1.92) .195 (.54) .552 (2.75)
DIP-Hi .424 (2.14) .617 (2.40) .179 (.95) .806 (3.00)
DIP-UPP .621 (2.10) .221 (.45) .551 (1.83) .175 (.32)
DIP-TECH .002 (.02) .031 (.17) -.036 (-.32) .071 (.35)
RRR .108 (.93) .147 1.82) .195 (1.44) .091 (1.15)
YRS-EL .035 (.60) .018 (.37) .002 (.02) .048 (1.00)
YRS-H1 .205 (4.08) .012 (.21) .152 (2.85) .047 (.76)
YRS-H2 -.040 (-.42) -.101 (-.60) -.010 (-.10) -.067 (-.35)
YRS-UN .205 (5.66) .300 (4.21) .160 (4.74) .307 (3.91)
YRS-TECH .036 (1.34) .098 (2.42) .020 (.60) .112 (2.47)
YRS-APP .067 (1.85) -.008 (-.31) .014 (.27) .010 (.38)
EXPOCC .087 (4.79) .116 (7.32) .053 (2.70) .127 (7.47)
EXPOCCQ x 100 -.868 (-1.43) -2.258 (-4.14) -.513 (-.79) -2.321 (-3.82)
GEXPER .016 (.94) .024 (1.43) -.023 (-1.28) .053 (3.15)
GEXPERQ x 100 .711 (1.73) -.089 (-.25) 1.007 (1.95) -.489 (-1.31)
a .704 (8.33) .814 (9,57) .389 .69211
i E .948 (34.18) -.746 (9.48)
Log-Likelihood -780.802
R .631 .609
*Estimates of parameters in the associated switching equation are reported in Table 3.3.
- 38 -
the same effects on the wage rates in the public and the private sector. The
years of schooling variables, however, showed a statistically significant
2difference between both sectors (X = 15.53, significant at 1 percent level).
Estimates of the thus restricted model are presented in Table 3.2.
The results of years of elementary schooling show no effect in both sectors.
Note, however, that the basic cognitive skills acquired in elementary school
show a significant effect (.125, T = 2.03). Junior high school years are
rewarded in the public but not in the private sector, university training is
valuable in both sectors but technical training pays only in the private
sector.
The results reported in Tables 3.1 and 3.2 clearly show the
selectivity bias in the OLS estimates. OLS equations estimate wages in both
sectors very well in view of the high R2 and high t-statistics. They produce
an estimated wage profile for public sector employees that lies substantially
above that in the private sector. 8/ The FIML estimates correct for
selectivity bias and show that this bias is large. The (marginal) wage
profiles now lie at the same level. The estimated variance rises, especially
that for the public sector equation, which is an indication that the sample of
public sector employees is a severely censored one. Finally, the correlation
coefficients between the disturbance in the switching equation (E) on the one
hand and those in the two wage equations (ul and u2) on the other, are highly
significant. Their signs conform to what one would expect based on the
definition of e = u1 - u2 - el. They imply that someone with, for some
unobserved reason, a high public (private) wage is also more likely to obtain
employment in the public (private) sector. Again, this effect of unobserved
factors is the reason for selectivity bias in the OLS results. As is well-
- 39 -
Table 3.2: FIML Estimates of Log Wage Equations forThe Public and Private Sector, with Restrictions on Diploma
and Basic Skill Variables*(t-values in parentheses)
Public Sector Private Sector
INTERCEPT 3.169 (12.03) 3.412 (18.27)
NAT _ .288 (2.10)
SEX -.112 (-.93) .148 (1.06)
DIP-EL** .450 (2.88)
DIP-HI** .487 (3.37)
DIP-UPP** .482 (2.07)
DIP-TECH** .001 (.01)
RRR** .125 (2.03)
YRS-EL .035 (.86) .032 (.84)
YRS-H1 .189 (4.24) .041 (.83)
YRS-H2 -.013 (-.16) -.145 (-1.39)
YRS-UN .207 (6.16) .295 (4.32)
YRS-TECH .037 (1.35) .107 (2.92)
YRS-APP** .009 (.43)
EXPOCC** .100 (8.06)
EXPOCCQ x 100** -.153 (-3.59)
CEXPER** .021 (1.84)
CEXPERQ x 100 ** .016 (.62)
aii .670 (7.41) .821 (9.11)
,lie .925 (25.18) -.741 (-8.91)
Log-Likelihood -785.99
* Estimates of parameters in the associated switching equation arereported in Table 3.3.
** Coefficients for these variables are restricted to be the same inboth sectors.
- 40 -
known (e.g. Goldberger, 1983) corrections for such bias may be sensitive to
the distributional assumptions adapted, as well as to the specification (and
consequent identification) of the switching equation. Still, given the
prevalence of OLS over FIML in this type of research, our conclusions sound a
strong warning against the use of results obtained without taking the
endogeneity of sector choice into account.
3.2 The Switching Equation
Given the estimation results we can now calculate expected log-wage
rates in both sectors and, using their differences as an instrumental
variable, estimate the structural switching equation as a probit equation.
Together with the FIML estimates of the switching equation related to Tables
3.1 and 3.2, respectively, these probit results are presented in Table 3.3, in
column 3. Note that the first two columns combine the effect of the personal
characteristics on wages with their impact on the likelihood of obtaining a
public sector job.
As became already apparent from the descriptive statistics, women are
relatively favored over men in the public sector. The schooling variables
play an important role in determining whether an individual obtains a public
or private job. That by itself is not a very surprising result: the skill
mix necessary in the government sector is more likely to show a need for
higher educated workers than that in the private sector. What is important,
however, is which of the schooling variables are dominating the selection
process. Elementary and high-school diplomas are extremely important, but
higher and technical diplomas have no significant effect.
- 41 -
Table 3.3: Estimates of the Switching Equation*(t-values in parentheses)
(1) (2) (3)FIML-Estimates FIML-Estimates Probit-Estimates
INTERCEPT -4.384 (-6.27) -4.731 (-6.85) -4.872 (5.10)SEX .423 (2.70) .414 (2.67) .456 (2.21)DIP-EL .615 (2.19) .525 (2.26) .824 (2.54)DIP-Hl .372 (1.72) .444 (2.50) .645 (2.31)DIP-UPP -.219 (-.79) -.324 (-1.38) -.185 (-.63)DIP-TECH .016 (.10) .028 (.21) -.007 (-.04)RRR -.124 (-1.21) -.116 (-1.38) -.144 (-1.26)YEARS SCHOOLING .048 (1.30) .048 (1.53) .036 (.86)AGE .165 (4.47 .191 (5.19) .183 (3.55)AGEQ * 100 -.158 (3.26) -.199 (-4.13) -.177 (-2.59)
Zyl - ZY2 -.307 (-.81)
' Columns (1) and (2) are associated with the estimated wage equationsreported in Tables 3.1 and 3.2, respectively.
Perhaps the most important result is that years of schooling do not
show any significant effect on the probability of obtaining a public job. All
that seems to matter is whether or not a diploma is obtained. Thus, screening
on the basis of diplomas obtained, seems to be the dominant case in Cate
d'Ivoire.
Age shows the positive effect that can be expected if there is
queuing for the public sector. Only at fairly high ages (around 50) becomes
the effect negative. In the structural probit equation the diploma effects
become even more pronounced, but the effect of the expected wage differential
is not significantly different from zero. The probit equation predicts the
probability of being in one sector or the other quite well. Over 71 percent
of the observations are predicted correctly.
- 42 -
4. Summary and Discussion
The estimation results of Tables 3.2 and 3.3 (column 2) allow us to
examine the large observed wage gap between the public and private sector.
For an average Ivorian employee, choosing to work in the public sector, we
predict a log wage of 6.246 (CFA 516). An identical employee who chooses to
work in the private sector would earn CFA 471 (log wage is 6.154). Clearly,
the larger differences in wage rates that are observed between both sectors
reflect to a large extent differences in educational attainment and
experience. We present the effect of changes in educational attainment and
occupation-specific experience on an otherwise average Ivorian employee, in
Table 4.1.
The first column shows differences in the probability of obtaining a
public job. The impact of education and age is very pronounced. Columns 2
and 4 show the differences in the offered log wage 91 in the public and
private sector, respectively. For example, someone with reading, writing and
arithmetic skills (RRR = 3) and 6 years of elementary schooling (and having
average characteristics otherwise) will be offered a log wage of 4.714 in the
public sector. If this person obtains an elementary school diploma, the offer
becomes 5.164. Wage offers in the private sector are higher for low education
levels, but lower for high education levels, quite different from what the OLS
results suggest.
In columns (3) and (5) we present the expected value of the log wage
rates conditional upon being in the public or the private sector. For the
public sector this is defined as
E(Qn w|I=l) = E(Un wle > - X6) = ZY1+ Pic 1/2 (X)
- 43 -
and for the private sector
E(Qn w|I = 0) = E(Qn w|e < XB) = ZY2 P2 .' a22 1_s(XS)
Table 4.1: Variations in the Probability of Obtaining a Public
Job and in the Differences in the Log Wage Offers, Mean
Observed Log Wages and Overall Average Log Wages
Among Ivorian Employees
(1) (2) (3) (4) (5) (6)
Public Observed Private Observed Overall
PR (Public Log-Wage Public Log-Wage Private Average
Job) Offer Log-Wage Offer Log-Wage Log-Wage
RRR YRSCH DIP-EL DIP-HI
O 0 0 0 .270 4.129 5.056 4.508 4.812 4.878
3 0 0 0 .169 4.503 5.633 4.882 5.085 5.178
3 6 0 0 .251 4.714 5.674 5.073 5.359 5.438
3 6 1 0 .442 5.164 5.840 5.523 5.998 5.928
3 10 1 0 .519 5.920 6.502 5.687 6.243 6.377
3 10 1 1 .688 6.407 6.796 6.173 6.935 6.839
3 14 1 1 .753 6.354 6.672 5.593 6.451 6.617
EXPOCC AGE0 25 .294 4.937 5.824 4.969 5.297 5.452
10 35 .571 5.779 6.300 5.811 6.425 6.354
20 45 .691 6.314 6.700 6.346 7.112 6.827
30 55 .664 6.542 6.958 6.574 7.303 7.074
- 44 -
where * and 0 are the standard normal density and cumulative distribution,
respectively. Note once again that for p1 = p2 = 0 these conditional
expectations are equal to the unconditional (marginal) ones, i.e. equal
to Zy1 and ZY2 respectively. However, the estimation results show that
(Pi.. a11/2 ) = .757 and (P1,* .22 1/2 ) = -.761, consequently results in i
columns (3) and (5) differ quite substantially from those in columns (2) and
(4), respectively. Finally, in column (6), we present the overall expected
wage rate, being the weighted average of the two conditional expectations.
Though OLS estimates based on sector specific samples show much
higher wage rates in the public sector, our estimates - taking the selection
process into account - do not show such dominance of public wage offers over
private ones. This does raise the ex-post question whether our model that
described the selection process was wrong to begin with. We think it is
not. For instance, in addition to possible wage differentials there may be
numerous other factors that make a public job preferable over a private one,
such as job security, working hours, fringe benefits. Table 4.2 shows
evidence of this: e.g. 92 percent of the public sector workers report a
retirement pension as part of their work compensation, and 90 percent has paid
sick leave. The corresponding percentages in the private sector are 41 and 49
percent, respectively. lo/
Given these non-wage benefits, workers may prefer the public sector
even if monetary wages are higher in the private sector. If this is the case,
one may want to adjust the presentation of the model (e.g. in terms of a
trade-off between wage and non-wage benefits), but the estimation model used
in this study will be exactly the same.
- 45 -
Table 4.2: Non-Wage Benefits for Private andPublic Workers (Percentages)
Private Public
Union on the job 39.5 64.2
Signed contract forspecified wage 33.2 54.7
Job covered by minimumwage laws 47.2 92.9
Paid holidays 53.2 92.5
iPaid sick leave 48.5 90.1
Retirement pension 40.9 91.5
Social Security Benefits 27.0 62.7
Our main results show first of all that OLS estimates based on sector
specific samples are likely to be seriously biased. Given all measured
attributes, individuals sort themselves in the "right" sector, i.e. unmeasured
factors that have a positive impact on the probability of obtaining a job in,
say, the public sector, are also positively correlated with the wage obtained
in that sector.
Among the measured characteristics, diplomas rather than years of
education determine the probability of being in the public or private
sector. Returns to completed schooling and to years of experience are
essentially the same in both sectors, but returns to years of schooling are
statistically significantly different. Contrary to what the OLS results
suggests, wage offers in the public sector are not universally higher than in
the private sector.
- 46 -
NOTES
1. Blank (1985) recognizes this selection problem but does not pursue it inher empirical work. By the best of our knowledge Gyourka and Tracy (1986)are the first to deal with this problem in the context of a public/private- union/non-union multinomial logit choice model. We thank Morty Stelcnerfor bringing this reference to our attention.
2. We will discuss the model under the assumption that the public sector isthe preferred one, though--of course--that is irrelevant for the basicargument.
3. Though even this does not constitute a strong test for the screeningversus human capital hypothesis, since these skills can very easily betested and, as a signal of ability to the employer, may serve the samerole as an elementary school diploma.
4. There are no non-Ivorians in the Public Sector. However, as we will seebelow, nationality has a significant effect on wages in the privatesector.
5. The likelihood function is available from the authors upon request.
6. A chi-square test shows no statistical improvement in earlier test runs,if years of schooling is disaggregated by type. This could be the resultof the high correlation with diplomas acquired.
7. The model was also estimated with three separate dummy variables, one foreach skill. We found the same lack of significance while a chi-squaretest indicated no statistical improvement over the more restrictedmodel. Omitting RRR from the model completely is not warranted at asignificance level of 8 percent.
8. For example, an individual able to read, write and do arithmetic buthaving no formal education would earn more in the public sector throughouthis working life than a high school graduate in the private sector,according to the OLS estimates.
9. i.e., the marginal wage rates, E(ln w1) = Zyi and E(lnw2) = ZY2 as opposed
to the conditional wage rates E(ln wlpublic) and E(ln w|private) which are
given below.
10. Note the large percentage of union workers in the public sector.Unionization may be partly responsible for the favorable workingconditions in the public sector. In a further analysis of these data wewill focus on the effect of unionization in both sectors (compare Cyourkoand Tracy, 1986).
- 47 -
APPENDIX 1The Log Likelihood Function for the Switching Regression Model
The Model: w1 = Zyl + ul (2)*
W2 = ZY2 + U 2 (3)
I 1, public sector, if e - XO (4)(n, observations)
I = 0, private sector, if e - X3(n2 observations)
(u 1 , U2 , E) is N (0, Z)
= 11 al 2 a101= 2 022 02c
The likelihood of an individual being in either regime is:**
f(w; I=1) = *f_Xs h(ul, e) de
= ( (W_-Zyl; 0, ao1) * [S1 (Xs + - (w-Zy 1 ))]
_1/2
a,- exp { (W-ZY1), * 2 [1[1 Xs + PI 01/2 (w-Zy1)lI
* The equation numbers correspond to those in the text; note that fornotational simplicity w refers to the log of the wage rate.
** + (-C; i, T) is an univariate normal density function with meanj and variance T * * (-) is a standard normal cumulative distribution.
- 48 -
and
f(w; I = O)
_1/2
°22 exp 1 (w Z 2)2 } * [1-2 [(1 - P2 W/Y{X2 + P )2 (W )}/ 2 7" 022
where: S. = a - aIa.. i = 1, 2
a =1e£
p. = a. /(a. a )1/2 i = 1, 215 1£ 1i ££
The log-likelihood function then reads:
ni nl+n2
L = Z En f (w; I=1) + E in f (w; I=O)i=l i=n1 +l
(n1-n2) n1 n2= ,, g,n 2r -2-Qn all - 2 Qna2 12 2 2
ni{n 1 )2 [( 2 _1/2( -1/2( )+ E {( 2Z, in '0 [ _lp) I ( + p 1 oal 2 (w-Zy1 ))I
n2+n2n2+ n2 £ { 1 2 2 -12/ + 12
+ E (W-ZY2) + in [1 - 0 [(l-P2e) /(XS + p2 e a22 (W-ZY2))I]}i=l+1 'C2 22
- 49 -
REFERENCES
Ainsworth, Martha and J. Mufioz, 1986, The C6te d'Ivoire Living StandardsSurvey; Design and Implementation, LSMS Working Paper No. 26, World Bank.
Blank, Rebecca M., 1985, An Analysis of workers' choice Between Employment inthe Public and Private Sector. Industrial and Labor Relations Review, 38,211-224.
Boissiere, M., J. B. Knight, R. H. Sabot, 1985 Earnings, Schooling, Abilityand Cognitive Skills, American Economic Review, New York.
Corbo, Vittorio and M. Stelcner, 1983, Earnings Determination and LaborMarkets, Journal of Development Economics, 12, 251-266.
Ganderson, 1979, Earnings Differentials between the Public and PrivateSectors. Canadian Journal of Economics, 12, 228-242.
Goldberger, Arthur, 1983. Abnormal Selection bias, in S. Karlin and T.Amemiya, eds. Studies in Econometrics, Time Series and MultivariateStatistics, Academic Press, New York.
Gyourko, Joseph and J. Tracy, 1986. An Analysis of Public and Private SectorWages Allowing for Endogenous Choices of Both Government and UnionStatus. NBER, Working Paper 1920.
Hav.eman and Wolfe, 1984. Schooling and Economic Well-being: The Role ofNon-Market Effects. Journal of Human Resources, 19, 377-407.
Krueger, Ann O., 1968, Factor Endowments and Per Capita Income DifferencesAmong Countries, Economic Journal, 78, 641-59.
Lee, L. F., 1978, Unionism and Wage Rates: A Simultaneous Equations Modelwith qualitative and Limited Dependent Variables. International EconomicReview, 415-433.
Lindauer, D. L. and R. Sabot, 1983. The Public/Private Wage Differential in aPoor Urban Economy. Journal of Development Economics 12, 137-152.
Mincer, Jacob, 1974, Schooling, Experience and Earnings. National Bureau ofEconomic Research, New York.
Psacharopoulos, George, 1985, Returns to Education: A Further InternationalUpdate and Implications. Journal of Human Resources, 20, 583-597.
Riley, John G., 1979, Testing the Educational Screening Hypothesis. Journalof Political Economy, 87, 5227-5252.
Robinson, Chris and N. Tomes, 1984, Union Wage Differentials in the Public andPrivate Sector: A simultaneous equations Specification. Journal of LaborEconomics, 2,106-127.
- 50 -
Smith, S. 1977, Equal Pay in the Public Sector: Fact or Fantasy? Princeton,New Jersey.
Van der Gaag, J., W. Vijverberg, 1986. Wage Determinants in Cate d'Ivoire;Experience, Credentials and Human Capital, Development ResearchDepartment, World Bank, Mimeo.
LSMS Working Papers (continued)
No. 28 Analysis of Household Expenditures
No. 29 The Distribution of Welfare in C6te d'Ivoire in 1985
No. 30 Quality, Quantity, and Spatial Variation of Price: Estimating Price Elasticities from Cross-sectional Data
No. 31 Financing the Health Sector in Peru
No. 32 Informal Sector, Labor Markets, and Returns to Education in Peru
No. 33 Wage Determinants in C6te d'Ivoire
No. 34 Guidelines for Adapting the LSMS Living Standards Questionnaires to Local Conditions
No. 35 The Demand for Medical Care in Developing Countries: Quantity Rationing in Rural C6te d'lvoire
No. 36 Labor Market Activity in C6te d'Ivoire and Peru
No. 37 Health Care Financing and the Demand for Medical Care
No. 38 Wage Determinants and School Attainment among Men in Peru
No. 39 The Allocation of Goods within the Household: Adults, Children, and Gender
No. 40 The Effects of Household and Community Characteristics on the Nutrition of Preschool Children: Evidencefrom Rural C6te d'Ivoire
No. 41 Public-Private Sector Wage Differentials in Peru, 1985-86
No. 42 The Distribution of Welfare in Peru in 1985-86
No. 43 Profits from Self-Employment: A Case Study of C6te d'Ivoire
No. 44 Household Surveys and Policy Reform: Cocoa and Coffee in the C6te d'lvoire
No. 45 Measuring the Willingness to Pay for Social Services in Developing Countries
No. 46 Nonagricultural Family Enterprises in C6te d'Ivoire: A Descriptive Analysis
No. 47 The Poor during Adjustment: A Case Study of the C6te d'Ivoire
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